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th2onnx.py
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th2onnx.py
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import os
import torch
from th_models import TorchHiFiGAN, TorchPromptTTS, TorchSimBert
from th_models import get_models
def export_voc_model(voc_model, voc_onnx_path='outputs/onnx/voc.onnx', inputs_dict=None):
print(inputs_dict.keys())
for param in inputs_dict.values():
print(param.size())
torch.onnx.export(
model = voc_model,
args = inputs_dict,
f = voc_onnx_path,
export_params = True,
opset_version=19,
verbose = False,
input_names = ["logmel"],
output_names = ["wav"],
do_constant_folding=True,
dynamic_axes = {
'logmel': {1: 'seq_len'}
},
)
def export_am_split_model(
am_mdoel,
am_encoder_onnx_path = "outputs/onnx/am_encoder.onnx",
am_decoder_onnx_path = "outputs/onnx/am_decoder.onnx",
inputs_dict = None,
):
print(inputs_dict.keys())
for param in inputs_dict.values():
print(param.size())
encoder_outputs = am_mdoel.encoder_forward(**inputs_dict)
print("am_outputs:", encoder_outputs[0].size(), encoder_outputs[1].size())
am_mdoel.forward = am_mdoel.encoder_forward
# torch.save({"x": encoder_outputs[0], "d_outs": encoder_outputs[1]}, "outputs/inputs_2_am_encoder_out.pt")
dynamic_axes = {
"inputs_ling": {1: "seq_len"},
# "inputs_style_embedding": {0: "bs"},
# "inputs_content_embedding": {0: "bs"},
}
torch.onnx.export(
model = am_mdoel,
args = inputs_dict,
f = am_encoder_onnx_path,
export_params = True,
opset_version=19,
verbose = False,
input_names = ["inputs_ling", "inputs_speaker", "inputs_style_embedding", "inputs_content_embedding"],
output_names = ["x", "d_outs"],
do_constant_folding=True,
dynamic_axes = dynamic_axes,
)
# export_am_decoder:
# param_dict = torch.load("inputs_2_am_encoder_out.pt", map_location='cpu')
# for name, param in param_dict.items():
# print(name, ":", param.size())
am_outputs = am_mdoel.decoder_forward(encoder_outputs[0], encoder_outputs[1])
print("am_decoder_outputs:", am_outputs.size())
am_mdoel.forward = am_mdoel.decoder_forward
dynamic_axes = {
"x": {1: "seq_len"},
"d_outs": {1: "seq_len"}
}
torch.onnx.export(
model = am_mdoel,
args = encoder_outputs,
f = am_decoder_onnx_path,
export_params = True,
opset_version=19,
verbose = False,
input_names = ["x", "d_outs"],
output_names = ["logmel"],
do_constant_folding=True,
dynamic_axes = dynamic_axes,
)
return 0
def get_style_embedding(prompt, tokenizer, style_encoder):
prompt = tokenizer([prompt], return_tensors="pt")
input_ids = prompt["input_ids"]
token_type_ids = prompt["token_type_ids"]
attention_mask = prompt["attention_mask"]
with torch.no_grad():
output = style_encoder(
input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
)
# print(output)
torch.save(prompt, "outputs/embedding_inputs.pt")
torch.save({"style_embedding": output}, "outputs/embedding_outputs.pt")
# style_embedding = output["pooled_output"].cpu().squeeze().numpy()
style_embedding = output.cpu().squeeze().numpy()
return style_embedding
def export_embedding(style_encoder, tokenizer, embed_onnx_path = "outputs/onnx/embedding.onnx", ):
# (style_encoder, generator, tokenizer, token2id, speaker2id) = get_models()
prompt = "你好,明天天气怎么样?"
outputs = get_style_embedding(prompt=prompt, tokenizer=tokenizer, style_encoder=style_encoder)
inputs = torch.load("outputs/embedding_inputs.pt", map_location='cpu')
torch.onnx.export(
model = style_encoder,
args = inputs['input_ids'],
f = embed_onnx_path,
export_params = True,
opset_version=19,
verbose = False,
input_names = ["input_ids", ],
output_names = ["style_embedding"],
do_constant_folding=True,
dynamic_axes = {
'input_ids': {1: 'seq_len'}
},
)
def export_models():
models = get_models()
(style_encoder, generator, tokenizer, token2id, speaker2id) = models
style_encoder = style_encoder.cpu().eval()
generator = generator.cpu().eval()
am_mdoel = generator.am
voc_model = generator.generator
am_mdoel = TorchPromptTTS(am_mdoel)
voc_model = TorchHiFiGAN(voc_model)
style_encoder = TorchSimBert(style_encoder)
am_inputs = torch.load("outputs/inputs_2.pt", map_location='cpu')
voc_inputs = torch.load("outputs/logmel_2.pt", map_location='cpu')
export_embedding(style_encoder, tokenizer)
export_am_split_model(am_mdoel, inputs_dict=am_inputs)
export_voc_model(voc_model, inputs_dict=voc_inputs)
if __name__ == "__main__":
export_models()